2510.0039 Uncertainty Quantification in Machine Learning for Responsible AI v1

🎯 ICAIS2025 Accepted Paper
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Accept

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📋 Summary

This paper introduces UQ-Net, a unified probabilistic framework designed to enhance uncertainty quantification (UQ) in large language models (LLMs). The core contribution lies in the integration of Bayesian modeling, calibration techniques, conformal prediction, and selective decision rules within a single framework. This integration aims to disentangle epistemic and aleatoric uncertainties, thereby supporting more reliable decision-making in the context of LLMs. The authors argue that current evaluation practices for UQ in LLMs are inadequate, highlighting issues such as the misalignment of consistency and entropy with factuality, the lack of benchmarks for multi-episode interactions, and inconsistent metrics for calibration and tightness. To address these shortcomings, they advocate for context-aware datasets, standardized metrics, and human-in-the-loop evaluations. The proposed UQ-Net framework is presented as a means to improve calibration and reduce predictive errors, with a focus on safety-sensitive applications. The paper presents two case studies, one in medical diagnosis and another in code generation, to demonstrate the effectiveness of UQ-Net. In the medical diagnosis case study, the authors use synthetic datasets to simulate medical diagnosis tasks, while in the code generation case study, they focus on improving task sequencing accuracy in robotic software engineering workflows. The empirical results from these case studies suggest that UQ-Net can achieve better calibration and reduce predictive errors compared to standard deep neural network (DNN) baselines. Specifically, the Bayesian UQ component of UQ-Net, using Monte Carlo Dropout, is shown to achieve a lower Expected Calibration Error (ECE) compared to baseline DNNs. Furthermore, the multi-episode modeling component of UQ-Net is reported to improve task sequencing accuracy by 18% over single-episode approaches. The authors also present a comparison of UQ performance metrics across different methods, including baseline DNNs, Bayesian UQ with dropout, multi-episode UQ, and conformal prediction. Overall, the paper aims to provide a principled foundation for operationalizing UQ in LLMs, advancing the development of trustworthy and responsible AI for real-world applications. The authors emphasize the importance of addressing the identified gaps in current evaluation practices and propose UQ-Net as a step towards more reliable and safer deployment of LLM agents.

✅ Strengths

This paper makes several valuable contributions to the field of uncertainty quantification in large language models. First, it provides a comprehensive review of the current state of UQ in LLMs, synthesizing insights from a wide range of sources and offering a broad perspective on the challenges and opportunities in this area. This review is particularly useful for researchers and practitioners looking to understand the current landscape of UQ for LLMs. Second, the introduction of UQ-Net as a unified probabilistic framework is a significant contribution. By combining Bayesian modeling, calibration, conformal prediction, and selective decision rules, the authors propose a more principled approach to UQ in LLMs. This integration is a novel approach that addresses the need for a more holistic method for handling uncertainty in these complex models. The paper also identifies critical gaps in existing evaluation practices, such as the misalignment of consistency and entropy with factuality, the lack of benchmarks for multi-episode interactions, and inconsistent metrics for calibration and tightness. These are important issues that need to be addressed to advance the field, and the paper's identification of these gaps is a valuable contribution in itself. Furthermore, the paper's advocacy for context-aware datasets, standardized metrics, and human-in-the-loop evaluations is a practical and actionable recommendation that can help to better align UQ methods with deployment needs. These recommendations are crucial for ensuring that UQ methods are not only theoretically sound but also practically useful in real-world applications. Finally, the empirical results presented in the paper, while limited in scope, do provide some evidence of the effectiveness of the proposed UQ-Net framework. The case studies in medical diagnosis and code generation demonstrate that UQ-Net can achieve better calibration and reduce predictive errors compared to standard baselines. The quantitative results, such as the improvement in Expected Calibration Error (ECE) and task sequencing accuracy, provide some support for the claims made about the framework's superiority. The inclusion of these empirical results, even though they are based on synthetic datasets, is a strength of the paper, as it provides some validation of the proposed approach.

❌ Weaknesses

Despite the strengths of this paper, several weaknesses warrant careful consideration. First, while the paper presents empirical results, the lack of detailed implementation guidelines significantly hinders reproducibility. The paper describes the components of UQ-Net, including Bayesian UQ, multi-episode interaction modeling, and red teaming validation, but it does not provide specific details on the architectures used, the training procedures, or the integration steps. For example, the exact architecture of the Bayesian network, the specific training procedures for the calibration model, and the precise steps for integrating the different components are not provided. The mention of Monte Carlo Dropout is brief and doesn't detail its specific implementation. This lack of detail makes it difficult for other researchers to reproduce the results and build upon the work. This is a critical limitation, as reproducibility is a cornerstone of scientific research. My confidence in this weakness is high, as it is directly evident from the lack of specific implementation details in the paper. Second, the paper's experimental scope is limited, focusing primarily on two specific domains: medical diagnosis and code generation. While these are relevant and important domains, the lack of broader testing across diverse LLMs and tasks reduces confidence in the generalizability of the findings. The paper does not explore the performance of UQ-Net in other domains, such as natural language understanding, information retrieval, or other areas where LLMs are commonly used. This narrow focus limits the applicability of the proposed framework to a wider range of machine learning problems. My confidence in this weakness is high, as it is directly observable from the limited scope of the experiments presented in the paper. Third, the paper's theoretical rigor, particularly in the areas of Bayesian modeling and conformal prediction, is somewhat limited. While the paper mentions the core concepts and provides a basic formula for Bayesian UQ, it does not delve into the deeper theoretical underpinnings, assumptions, or convergence properties of the Bayesian methods or the formal guarantees of the conformal prediction. The description of Monte Carlo Dropout is brief and lacks detail. This lack of theoretical depth may impact the framework's robustness and generalizability. My confidence in this weakness is medium, as it is based on the lack of in-depth theoretical development and proofs in the paper. Fourth, the paper does not adequately address the computational demands of UQ-Net, especially given the use of Bayesian methods and conformal prediction. These techniques can be computationally intensive, and the paper does not discuss the computational complexity or scalability of the framework. The mention of high-performance GPUs in the experimental setup hints at potential computational demands, but there is no explicit discussion of the computational cost. This lack of attention to scalability raises concerns about the practical applicability of UQ-Net in large-scale applications. My confidence in this weakness is high, as it is based on the absence of any discussion of computational complexity or scalability in the paper. Fifth, the paper's recommendations for addressing the identified gaps in evaluation practices are somewhat high-level and lack specific, actionable steps. While the paper advocates for context-aware datasets, standardized metrics, and human-in-the-loop evaluations, it does not provide concrete details on how to implement these recommendations. For example, the paper does not specify which metrics should be standardized or provide protocols for human-in-the-loop evaluations. This lack of specificity makes it difficult to translate these recommendations into practical actions. My confidence in this weakness is high, as it is directly evident from the high-level nature of the recommendations in the paper. Finally, while the paper does include some baseline comparisons, it might be missing comparisons to the most recent and relevant UQ methods for LLMs. The paper compares against standard DNNs and some UQ techniques, but it might be missing comparisons to more cutting-edge or specialized UQ methods for LLMs that have emerged recently. This lack of comparison to the most recent methods weakens the evidence of UQ-Net's advantages. My confidence in this weakness is medium, as it is based on the absence of comparisons to the most recent and relevant UQ methods for LLMs.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. First, the authors should provide detailed implementation guidelines to facilitate reproducibility. This includes specifying the exact architectures used for the Bayesian model, the calibration model, and the conformal prediction model, as well as the training procedures for each component. The authors should also provide details on how these components are integrated, including the specific data flows and the optimization algorithms used. Furthermore, the authors should provide access to the code and baselines used in the experiments to allow other researchers to reproduce the results and build upon the work. This would greatly enhance the credibility and impact of the paper. Second, the authors should expand the experimental scope to include a broader range of LLMs and tasks. This could involve testing UQ-Net in other domains, such as natural language understanding, information retrieval, or other areas where LLMs are commonly used. This would provide a more comprehensive evaluation of the framework's generalizability and applicability. Third, the authors should provide a more rigorous theoretical treatment of the Bayesian modeling and conformal prediction components. This would include a more detailed derivation of the posterior distribution, a discussion of the assumptions made, and an analysis of the convergence properties of the inference algorithm. For conformal prediction, the paper should provide a formal proof of the validity of the confidence intervals, a discussion of the assumptions underlying the method, and an analysis of the tightness of the intervals. This would strengthen the theoretical foundation of the work and provide a better understanding of the conditions under which the framework is expected to perform well. Fourth, the authors should address the computational demands of UQ-Net. This could involve exploring techniques for reducing the computational cost of Bayesian inference and conformal prediction, such as using more efficient sampling methods or approximation techniques. The paper should also include a discussion of the computational resources required to train and deploy the framework, as well as any techniques used to reduce the computational cost. Fifth, the authors should provide more concrete solutions and recommendations for addressing the identified gaps in evaluation practices. Instead of simply stating the problems, the authors should propose specific, actionable steps for how these problems can be solved. For example, the paper could propose a set of standardized metrics for evaluating uncertainty quantification methods, along with a detailed explanation of how these metrics should be used. The authors could also propose a framework for developing context-aware datasets, including specific guidelines for how to collect and annotate data that is relevant to real-world applications. Furthermore, the paper could provide a detailed description of how human-in-the-loop evaluations can be conducted, including specific protocols for how to involve human evaluators in the evaluation process. Finally, the authors should include comparisons to the most recent and relevant UQ methods for LLMs. This would provide a more comprehensive evaluation of UQ-Net's performance and demonstrate its advantages over existing methods. This could involve comparing UQ-Net against methods like deep ensembles, Monte Carlo dropout, or other specialized UQ techniques for LLMs, using metrics such as Expected Calibration Error (ECE), Negative Log-Likelihood (NLL), and Brier score.

❓ Questions

Several questions arise from my analysis of this paper. First, can the authors provide more details on the specific challenges and opportunities associated with applying UQ-Net to different types of machine learning models and applications beyond LLMs? While the paper focuses on LLMs, it would be valuable to understand how the framework could be adapted to other types of models and tasks. Second, how do the authors envision the proposed framework being used in practice, and what are the potential barriers to its adoption in real-world applications? This question seeks to understand the practical implications of the framework and the challenges that might arise during its deployment. Third, what are the most critical research gaps that need to be addressed to advance the field of UQ in LLMs, and how do the authors plan to address these gaps in their future work? This question aims to understand the authors' perspective on the future directions of research in this area. Fourth, could the authors elaborate on the theoretical foundations of UQ-Net, particularly regarding the integration of epistemic and aleatoric uncertainties? This question seeks to understand the theoretical underpinnings of the framework and the assumptions made during its development. Fifth, why were specific domains (e.g., medical diagnosis, code generation) chosen for the experiments, and how do the authors plan to validate UQ-Net across a broader range of applications? This question aims to understand the rationale behind the choice of experimental domains and the plans for future validation. Finally, can the authors provide more specific implementation guidelines, including architectural choices, training procedures, and integration steps for UQ-Net? This question seeks to address the lack of implementation details in the paper and facilitate reproducibility.

📊 Scores

Soundness:2.75
Presentation:2.5
Contribution:2.5
Rating: 5.25

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📋 Summary

The paper surveys uncertainty quantification (UQ) for large language models (LLMs) and proposes UQ-Net, a unified framework that combines Bayesian modeling, calibration, conformal prediction, and selective decision rules. A key claimed contribution is multi-episode uncertainty aggregation (Eq. 2) for agentic LLM workflows in software engineering (SE). The paper argues that disentangling epistemic and aleatoric uncertainty, integrating anomaly detection, and enabling selective prediction can improve safety and reliability. It reports case-study evidence (medical diagnosis and code generation) and synthetic experiments, claiming improved calibration (e.g., ECE 0.12 vs 0.25), 15–20% error reduction over baselines, 18% gains in multi-episode task sequencing, and 23% more vulnerabilities found via red teaming. The authors identify gaps in current practice (e.g., misalignment of entropy/consistency with factuality, lack of multi-episode benchmarks) and call for standardized, context-aware datasets and human-in-the-loop evaluations.

✅ Strengths

  • Timely and important topic: credible UQ for agentic LLMs in safety-sensitive domains (healthcare, SE).
  • Broad and useful synthesis of >80 papers, with a clear articulation of practical gaps (e.g., misalignment of entropy/consistency with factuality, lack of multi-episode and interaction-level benchmarks).
  • Proposes a unified view that couples UQ with calibration, conformal prediction, selective prediction, anomaly detection, and red teaming, emphasizing end-to-end deployment considerations.
  • Highlights selective prediction and the accuracy-coverage trade-off, and advocates human oversight and red teaming for safer deployment.
  • Introduces an explicit multi-episode UQ aggregation formulation (Eq. 2) for agentic workflows, an underexplored direction the community likely cares about.

❌ Weaknesses

  • Technical specificity is insufficient: UQ-Net is described conceptually, but core implementation details are missing (e.g., whether the approach is white-box or black-box; how Bayesian posterior is operationalized in LLMs; how conformal sets are defined for open-ended generation; how the episode weights w_t in Eq. 2 are obtained; how aleatoric vs epistemic are disentangled in practice for sequence generation).
  • Empirical evidence lacks rigor and reproducibility: results rely on undisclosed synthetic datasets with no details on generation, size, task definitions, splits, prompts, model identities/versions, or compute. There is no evaluation on community-standard benchmarks for hallucination/factuality, OOD detection, selective prediction, or calibrated code generation.
  • Several headline claims (15–20% calibration/error reductions, ECE 0.12 vs 0.25, 18% improvements in multi-episode settings, 23% more vulnerabilities) are not grounded in transparent protocols, baselines, or statistical analyses, limiting credibility.
  • The disentanglement of aleatoric vs epistemic uncertainty is asserted but not operationally demonstrated for LLMs (e.g., MC Dropout is non-trivial for modern LLM stacks; no details on approximation or alternatives such as ensembles or post-hoc calibration under black-box constraints).
  • Writing and organization issues: the paper mixes survey and method without a clear demarcation of contributions; some repeated or overlapping subsections; occasional placeholders and minor inconsistencies that impede clarity; reliance on illustrative figures rather than executable procedures.

❓ Questions

  • UQ-Net specification: Is the method intended to be white-box (access to weights/activations) or black-box (API only)? How are Bayesian approximations (e.g., Monte Carlo Dropout, ensembles) realized for LLMs in each setting?
  • Multi-episode aggregation (Eq. 2): How are the weights w_t learned or selected? Is there a principled derivation (e.g., from probabilistic graphical models or variance decomposition), or is it heuristic? Please provide an ablation showing sensitivity to w_t and T.
  • Disentangling epistemic vs aleatoric: What concrete estimators are used for each in the context of open-ended generation (code, QA, summarization)? How do you ensure identifiability, and what diagnostics verify the decomposition?
  • Conformal prediction for generative LLMs: What nonconformity scores do you use for free-form text/code? Do you operate at token, span, or semantic-equivalence levels (e.g., response set semantics)? How are coverage guarantees defined and measured in your tasks?
  • Selective prediction: How are thresholds chosen, and what is the coverage-accuracy curve on standard benchmarks? Do you compare to strong baselines such as deep ensembles, temperature scaling, Dirichlet calibration, or recent LLM-specific UQ methods?
  • Synthetic datasets: Please detail generators, domain assumptions, task specs, scales, splits, prompt templates, LLM identities/versions, and random seeds. What known distributions or OOD shifts are simulated? Can you release the generators and code?
  • Case studies: For medical diagnosis and code generation, what datasets (or synthetic approximations) and ground-truth labels were used? How was hallucination/OOD defined and detected? What human evaluation or clinical/code-review protocols, if any, were employed?
  • Red teaming: What attack types and protocols were used, and how is the 23% gain quantified? Are the findings robust across models and prompts? How does red teaming couple with UQ-Net to change coverage/accuracy or risk metrics?
  • Computational cost and latency: What is the overhead of multi-inference, ensembles, or conformal procedures relative to single-shot generation? How does this scale with episode length in multi-episode settings?
  • Benchmarks: Can you report standardized results on hallucination/factuality benchmarks (e.g., TruthfulQA, HaluEval), selective prediction settings for QA, and code generation (e.g., HumanEval/HumanEval-X) with calibration metrics (ECE, Brier, NLL) and OOD detection (AUROC/AUPR)?

⚠️ Limitations

  • Insufficient experimental transparency and lack of evaluation on community-standard benchmarks limit external validity and reproducibility.
  • Potential computational overhead and latency from multi-sampling, ensembles, or conformal methods may hinder deployment in agentic settings; a systematic cost analysis is missing.
  • Selective prediction can reduce coverage and may introduce fairness concerns if rejection rates differ across subpopulations or task types; no subgroup calibration or fairness analysis is provided.
  • Disentanglement of aleatoric vs epistemic uncertainty in LLMs is challenging; the paper does not present validation that the estimated components correspond to intended phenomena.
  • Reliance on UQ proxies (e.g., entropy, consistency) may misalign with factuality; the paper acknowledges this gap but does not offer concrete mitigation beyond high-level guidance.
  • Potential negative societal impacts: misplaced confidence calibration in high-stakes domains (e.g., clinical, legal) could lead to overreliance; without rigorous validation and human-in-the-loop safeguards, risk may be underestimated.

🖼️ Image Evaluation

Cross‑Modal Consistency: 20/50

Textual Logical Soundness: 12/30

Visual Aesthetics & Clarity: 12/20

Overall Score: 44/100

Detailed Evaluation (≤500 words):

Visual ground truth (image‑first):

• Figure 1: Single scatter/line plot; axes “System Input X/Y”; green vs red points; dashed linear fit; extrapolation looks unreliable.

• Figure 2(a): Panel of divergent LLM arithmetic answers with scores (e.g., 0.69, −0.31) and small bar charts.

• Figure 2(b): Cartoon of recommendation network; probabilities P(Cat)=0.2, P(Dog)=0.8; novel input symbol.

• Figure 3: Line chart “Total Number of Publications… (2010–2025)” rising steeply.

• Figure 4(a): Mind‑map workflow: Data/Model → Training Algorithm → Trained Model + Test Data → outputs (Prediction, Biases, Uncertainty, Label Noise).

• Figure 4(b): Selective‑prediction flow; decision g(x)≥τ; accuracy‑coverage curve; p(y|x) sketch.

• Figure 5: Car/Truck training images; inference panels with 90%/92% “confidence”; deer flagged “UNCERTAIN”.

1. Cross‑Modal Consistency

• Major 1: Figure 4 is described as a multi‑episode, red‑teaming UQ framework, but visuals are generic risk/selection cartoons. Evidence: “Figure 4 illustrates a detailed … multi‑episode, red‑teaming environment.” (Sec 4.1) vs Fig. 4(a,b) labels.

• Major 2: Claimed ECE result tied to wrong table. Evidence: “achieving an Expected Calibration Error (ECE) of 0.12 … as shown in Table 1.” (Sec 4.1). Table 1 is a literature summary, not experiment results.

• Major 3: Placeholder citation. Evidence: “as conceptualized in Figure ??” (Sec 4, first paragraph).

• Major 4: Table mismatch. Evidence: Title “Table 2: Comparison of UQ Performance Metrics Across Methods” yet the table columns are “Area / Future Directions / src.” (Sec 4.1).

• Minor 1: Figure 3 trend is used to claim “publication trend on UQ (Figure 4)” (Sec 4.3) – wrong figure number.

• Minor 2: Negative “confidence” value (−0.31) in Fig 2(a) is unexplained in text, risking confusion.

2. Text Logic

• Major 1: Flagship improvements (15–20% calibration/error reduction) lack verifiable, consistent evidence. Evidence: “reduces predictive error by 15–20% relative to baselines.” (Abstract) with no matching quantitative figure/table.

• Major 2: “Multi‑episode modeling improved … 18%” has no experiment details (dataset, metrics, variance) and no aligned figure/table. Evidence: “improved task sequencing accuracy by 18%” (Sec 4.1).

• Minor 1: Section duplication/overlap (Sec 2.1 and 2.5 both titled about LLM uncertainty) muddles scope.

• Minor 2: Some claims rely on news/system‑card reports for GPT‑5 rather than peer‑reviewed evidence.

3. Figure Quality

• Minor 1: Several panels (Fig 2 and Fig 4) use small icons/text that are hard to read at print size; add larger fonts/legends.

• Minor 2: Fig 5 lacks a legend explaining how “confidence” is computed; unclear thresholding.

• Comprehension probe: Figures 4–5 need clearer call‑outs (definitions of g(x), τ, calibration method) to be self‑contained.

Key strengths:

• Topical and timely survey; clear decomposition of epistemic vs aleatoric with standard equations.

• Useful conceptual visuals for selective prediction and risk‑aware decision rules.

• Practical emphasis on SE/agentic settings and human‑in‑the‑loop validation.

Key weaknesses:

• Multiple figure/table misreferences and placeholder (“Figure ??”) break the evidence chain.

• Core quantitative claims lack aligned, reproducible results.

• Overreliance on conceptual diagrams; missing ablations, datasets, metrics, and uncertainty calibration plots (reliability diagrams, coverage vs risk).

📊 Scores

Originality:2
Quality:2
Clarity:2
Significance:2
Soundness:2
Presentation:2
Contribution:2
Rating: 4

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📋 Summary

The paper introduces UQ-Net, a novel framework for uncertainty quantification (UQ) in large language models (LLMs), addressing the critical need for reliable decision-making in high-stakes domains like software engineering. The authors identify the limitations of existing LLMs, such as overconfidence and sensitivity to out-of-distribution data, and propose UQ-Net as a solution that combines Bayesian modeling, calibration techniques, conformal prediction, and selective decision rules. This integration aims to disentangle epistemic uncertainty (from model parameters) and aleatoric uncertainty (from data noise), enhancing the trustworthiness of LLMs in tasks requiring high reliability. The paper also emphasizes the importance of context-aware datasets and standardized metrics for evaluating UQ methods, advocating for human-in-the-loop evaluations to align these methods with real-world deployment needs. Through case studies in medical diagnosis and code generation, the authors demonstrate UQ-Net's potential to improve calibration and reduce predictive errors, contributing to the development of trustworthy AI systems.

✅ Strengths

I find several aspects of this paper to be particularly strong. The most notable is the introduction of UQ-Net, a unified probabilistic framework that combines multiple techniques to address uncertainty quantification in LLMs. The integration of Bayesian modeling, calibration, conformal prediction, and selective decision rules is a novel approach that allows for a more comprehensive understanding of both epistemic and aleatoric uncertainties. This is a significant step forward, as it moves beyond simple point estimates and provides a more nuanced view of model confidence. The paper's focus on software engineering applications is also a strength. By addressing the specific challenges of uncertainty in this domain, the authors highlight the practical relevance of their work. The inclusion of multi-episode interaction modeling is another positive aspect, as it allows the framework to capture the historical context in iterative software engineering workflows, which is crucial for real-world applications. The use of red teaming for safety validation adds an extra layer of rigor to the evaluation process, demonstrating the authors' commitment to responsible AI development. Furthermore, the paper's call for context-aware datasets and standardized metrics for uncertainty quantification is a valuable contribution to the field. This highlights the need for more robust evaluation practices and encourages the community to move towards more reliable and comparable assessments of uncertainty quantification methods. Finally, the reported improvements in calibration and predictive error in the case studies provide empirical evidence for the effectiveness of the proposed framework, suggesting that UQ-Net has the potential to significantly improve the reliability of LLMs in safety-sensitive applications.

❌ Weaknesses

Despite the strengths, I have identified several weaknesses that significantly impact the paper's overall quality and clarity. First, the paper suffers from a lack of clarity in its writing and organization. The flow of ideas is often disjointed, making it difficult to follow the authors' line of reasoning. For example, the transitions between different sections and paragraphs are not always smooth, and the connections between concepts are not always clearly articulated. This lack of clarity is further exacerbated by the presence of numerous grammatical errors and typos throughout the text. These errors, while seemingly minor, detract from the overall professionalism of the paper and make it more difficult to understand the authors' intended meaning. For instance, I noted errors such as "inteligent" instead of "intelligent" and "ilustration" instead of "illustration" in the abstract alone. The inconsistent use of abbreviations also contributes to the lack of clarity. While the authors introduce abbreviations like UQ, LLM, and SE, they are not always used consistently throughout the paper, and in some cases, they are used before being fully defined. This inconsistent use of abbreviations can be confusing for the reader and makes it more difficult to follow the authors' arguments. Furthermore, the paper's structure is not always logical. The placement of the "Implications and Opportunities" section (Section 3.2) before the "Results and Contributions" section (Section 4) is unusual and disrupts the natural flow of the paper. Typically, results and contributions are presented before discussing their implications. This unconventional structure makes it harder to understand the context and motivation behind the implications being discussed. Another significant weakness is the lack of detail in the methodology section. While the paper introduces UQ-Net as a novel framework, the description of its components and their integration is not sufficiently detailed. For example, the paper mentions the use of Bayesian modeling, calibration, and conformal prediction, but it does not provide enough information on how these techniques are specifically implemented and combined within the UQ-Net framework. This lack of detail makes it difficult to fully understand the proposed method and to reproduce the results. The paper also lacks a thorough discussion of the limitations of the proposed approach. While the authors mention some limitations in the future directions section, they do not provide a comprehensive analysis of the potential drawbacks and challenges of UQ-Net. For example, the paper does not discuss the computational cost of the framework or its sensitivity to different types of data. Finally, the paper's experimental evaluation is not as robust as it could be. While the authors present case studies in medical diagnosis and code generation, they do not provide enough detail on the experimental setup and the specific datasets used. The lack of detailed experimental information makes it difficult to assess the validity of the results and to compare them with other methods. The paper also lacks a thorough comparison with existing uncertainty quantification techniques. While the authors mention some related work, they do not provide a detailed analysis of how UQ-Net compares to these existing methods in terms of performance, computational cost, and applicability. This lack of comparison makes it difficult to assess the novelty and significance of the proposed framework. The confidence level for each of these weaknesses is high, as they are all supported by direct evidence from the paper.

💡 Suggestions

Based on the identified weaknesses, I recommend several concrete improvements. First and foremost, the authors should thoroughly revise the paper for clarity and coherence. This includes improving the flow of ideas, correcting grammatical errors and typos, and ensuring consistent use of abbreviations. The authors should also reorganize the paper to follow a more logical structure. Specifically, the "Implications and Opportunities" section should be moved to after the "Results and Contributions" section. This will ensure that the implications are discussed in the context of the presented results. Furthermore, the methodology section needs to be significantly expanded to provide more detail on the implementation of UQ-Net. This includes providing more information on how the different components of the framework are integrated and how the specific parameters are chosen. The authors should also include a more thorough discussion of the limitations of the proposed approach. This includes discussing the computational cost of the framework, its sensitivity to different types of data, and any other potential drawbacks. The experimental evaluation also needs to be improved. This includes providing more detail on the experimental setup, the specific datasets used, and the evaluation metrics. The authors should also include a more thorough comparison with existing uncertainty quantification techniques. This comparison should include a discussion of the performance, computational cost, and applicability of each method. In addition to these specific recommendations, I suggest that the authors consider the following general principles for improving the clarity and rigor of their work: 1. Use clear and concise language. Avoid jargon and technical terms whenever possible. 2. Provide sufficient context for your arguments. Make sure that the reader understands the motivation behind your work and the significance of your findings. 3. Be precise and accurate in your descriptions. Avoid vague or ambiguous language. 4. Use appropriate formatting and organization. Make sure that your paper is easy to read and navigate. 5. Proofread your work carefully. Make sure that your paper is free of grammatical errors and typos. By following these principles, the authors can significantly improve the quality and clarity of their paper and make it more accessible to a wider audience.

❓ Questions

Several questions arise from my analysis of this paper. First, I am curious about the specific implementation details of the Bayesian modeling component within UQ-Net. The paper mentions the use of Bayesian methods to distinguish epistemic and aleatoric uncertainties, but it does not provide enough information on how these methods are specifically implemented. For example, what specific Bayesian techniques are used, and how are the prior distributions chosen? Second, I would like to know more about the specific calibration techniques used in UQ-Net. The paper mentions the use of calibration methods, but it does not provide enough detail on how these methods are applied. For example, what specific calibration algorithms are used, and how are they integrated with the other components of the framework? Third, I am interested in the specific details of the conformal prediction component of UQ-Net. The paper mentions the use of conformal prediction for systematic uncertainty assessment, but it does not provide enough information on how this technique is implemented. For example, what specific conformal prediction algorithms are used, and how are the confidence scores calculated? Fourth, I would like to understand the specific criteria used for selective prediction in UQ-Net. The paper mentions the use of selective decision rules, but it does not provide enough detail on how these rules are determined. For example, what specific thresholds are used, and how are these thresholds chosen? Finally, I am curious about the specific datasets used in the case studies. The paper mentions the use of synthetic datasets simulating SE tasks, but it does not provide enough detail on the characteristics of these datasets. For example, what specific tasks are simulated, and what are the key features of the datasets? These questions target core methodological choices and seek clarification of critical assumptions, which I believe are essential for a complete understanding of the paper.

📊 Scores

Soundness:2.0
Presentation:1.67
Contribution:1.67
Rating: 3.0

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